Abstract Adaptive path planning and optimization for robots are a persistent challenge due to the lack of a cognitive map of the complex and dynamic environments. This paper presents a cognitive‐map‐based method for dynamic robot path planning by a formal maze model for workplace layouts representation. The maze model and global path trees provide a cognitive map for the robot to aware the environment. A theory of dynamic Paths Finding and Optimization (PFO) is developed. Powered by the PFO algorithm, a robot is able to autonomously explore the optimal path in a complex workplace by a single sight‐read of its layout. The maze based PFO methodology provides robot for an efficient real time path cognition and optimization algorithm for dealing with dynamic workplaces. A set of experiments demonstrates the efficiency of the method, which extends traditional stepwise robot vision technologies to cognitive‐map‐driven global path optimization.

Particle swarm optimization has been extensively noticed for its fast convergence speed with few parameters. However, it would be plagued by the premature convergence only affected by the global particles. In this study, Composite Proactive Particles in Swarm Optimization (Co-PPSO) is proposed. In Co-PPSO, the composite strategy framework is embedded into Proactive Particles in Swarm Optimization (PPSO), that is, three learning strategies are proposed to evaluate their differences and select the most suitable one for each particle. In addition, an elite group is constructed to make the particles jump out of the situation that they are only affected by the global best one in the particle swarm, and further improve the convergence accuracy. CEC2022 competition of single objective bound-constrained numerical optimization is employed to test the effect of 10-$D$and 20-$D$optimization, and four well-known PSO variants were used for comparison. The experimental results show that the Co-PPSO has certain competitiveness to improve premature convergence.

Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution.

The particle swarm optimization algorithm is an effective tool to solve various optimization problems due to the small number of parameters and the simple learning strategy. However, the updated strategy from the basic PSO mainly aims to learn the global optimal particles, and it often leads to premature convergence with poor solution accuracy. An adaptive complex network topology with a fitness distance correlation for the particle swarm optimization algorithm is proposed (CNAPSO). Using the CNAPSO algorithm, it is concluded that different network topologies have different degrees of dispersion in the process of particle swarm optimization search. Therefore, the adaptive strategy with the fitness distance correlation proposes to effectively balance the global exploration and local exploitation capabilities, which is the particle swarm adaptive network neighborhood topology. The neighborhood topology construction strategy with a complex network is used to construct the neighborhood topology for each particle. Therefore, the local optimal particles in the neighborhood participate in the search process of particle swarm optimization and eliminate the situation of only learning the global optimal particles. Moreover, it improves the solution accuracy of the particle swarm optimization algorithm. In addition, to avoid the particle swarm falling into premature convergence, this study introduces a random drift strategy to make the particles drift slightly and reduces the risk of premature convergence. The experimental results on twenty-four benchmark functions show that CNAPSO has great improvements in the accuracy of the solution and the speed of convergence compared with the six representative PSO algorithms.

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.

Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-population evolutionary algorithm adapting to complementary evolutionary strategy (DPCEDT) based on the study of differential evolution algorithm, teaching and learning-based optimization algorithm. The simulation results show that the algorithm performs better than the TLBO-DE, HDT and DPDT and some other algorithms do in most test functions. It suggests that the complementary evolutionary strategies are more advantageous than other evolutionary strategies in dual-population evolutionary algorithms.

Differential evolution (DE) has been widely applied to complex global optimization problems. Different search strategies have been designed to find the optimum conditions in a fitness landscape. However, none of these strategies works well over all possible fitness landscapes. Since the fitness landscape associated with a complex global optimization problem usually consists of various local landscapes, each search strategy is efficient in a particular type of fitness landscape. A reasonable approach is to combine several search strategies and integrate their advantages to solve global optimization problems. This paper presents a new self-feedback strategy differential evolution (SFSDE) algorithm based on fitness landscape analysis of single-objective optimization problem. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitness-distance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. Moreover, mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy. Finally, the proposed SFSDE algorithm is implemented to optimize soil water textures, and the experimental results show that the proposed SFSDE algorithm reduces the difficulty in estimating parameters, simplifies the solution process and provides a novel approach to calculate the parameters of the Van Genuchten equation. In addition, the proposed algorithm exhibits high accuracy and rapid convergence and can be widely used in the parameter estimation of such nonlinear optimization models.

This paper describes the concept of workflow merge and methods for merging business processes. We append effect annotations to activities of business process, use RPSTs divided the process graph to fragments then accumulate these effects according to these SESE fragments, detect exact clone and approximate clone between the two process models, finally design a merging algorithm to consolidate two processes. It is shown that to avoid invalid merges, one choose merge unit is SESE fragments, we also raise issues of more complex merge problems, such as semantic annotations.